Multicloud AI & ERP / Cloud AI Applications & Industry AI Solutions
Why it matters
AI projects spend most of their time finding, cleaning and securing data. A unified platform with one governed copy turns that effort into a shared asset instead of repeating it for every use case.
How it works
From sources to AI products
Data flows from sources into a lakehouse with open tables and one copy. Governance adds the catalog, access controls and lineage. AI and analytics tools work directly on governed data, producing dashboards, apps and copilots.
AWS, Azure and Google Cloud compared
| AWS | Azure | Google Cloud | |
|---|---|---|---|
| Unified platform | SageMaker Unified Studio | Microsoft Fabric | BigQuery with Gemini |
| Lakehouse | SageMaker Lakehouse, S3 Tables | OneLake | BigQuery and BigLake |
| Replication | Zero-ETL | Mirroring | Datastream |
| Governance | DataZone, Lake Formation | Purview | Dataplex |
| Data agents | Amazon Quick | Fabric data agents | BigQuery data agents |
What makes each cloud different
AWS
SageMaker Unified Studio brings data engineering, analytics and Bedrock-based AI into one studio over SageMaker Lakehouse, with zero-ETL feeding data in and DataZone governing access.
Azure
Microsoft Fabric puts data engineering, warehousing, real-time analytics and Power BI on OneLake, with Purview governance and data agents that answer questions in plain language.
Google Cloud
BigQuery acts as Google's AI-ready data platform, with Gemini assistance, BigLake for open formats, Dataplex governance and conversational analytics agents.
Design decisions that matter
- 01One governed copy of core data
No shadow extracts.
- 02Open formats
Keep engines and clouds swappable.
- 03Governance built in
Catalog and access from the start.
- 04Data products for AI
Owned, documented, reusable.
Common mistakes we see
- A data copy per AI project. Inconsistent answers and security gaps.
- Governance as an afterthought. AI exposes data it should not.
- Platform without owners. Quality decays.
A practical 30-day plan
- Week 1
Pick the platform and the first data domain.
- Week 2
Replicate sources into open tables.
- Week 3
Apply catalog, access and lineage.
- Week 4
Build the first AI use case on the governed data.
How NTS helps
NTS builds AI-ready data platforms on SageMaker Unified Studio, Microsoft Fabric and BigQuery, covering architecture, migration, governance and the first AI use cases.
What you get
- A data platform that feeds analytics and AI
- Quality checks and governance for AI data
- Feature and embedding pipelines
- Access controls for sensitive data
Typical outcomes
- AI projects that start faster
- Higher-quality AI results
- Sensitive data protected
How we work with you
- 1Assess
A focused review of your data platform for AI across AWS, Azure and Google Cloud. We look for the gaps we find most often: a data copy per AI project, governance as an afterthought and platform without owners.
- 2Design
A target architecture and roadmap built on the decisions that matter most: one governed copy of core data, open formats, governance built in and data products for AI.
- 3Build
Hands-on delivery alongside your team. We pick the platform and the first data domain, replicate sources into open tables, apply catalog, access and lineage, and finally build the first AI use case on the governed data.
- 4Run and improve
We maintain pipelines, quality checks and access as new AI use cases arrive.
Why teams choose NTS for data platform for AI
- AI starts with data
We fix the data foundations that most AI projects lack.
- Governed and fast
Teams get quick access to trusted, approved data.
- Built once, used everywhere
The same platform serves BI, ML and GenAI.
Book a free 30-minute review
Talk to an NTS architect about your data platform for AI on AWS, Azure and Google Cloud.